Thomas D Swinburne (Tom)

CNRS Researcher, CiNAM, Luminy, Marseille
thomas "." swinburne "@" cnrs "." fr
CV  /  Google Scholar  /  GitHub

We’re hiring: PhD & Postdoc (ANR DAPREDIS) PhD Application

New approaches in computational metallurgy
Keywords: data-driven coarse-graining, uncertainty quantification, atomic simulation, rare event sampling, dislocation plasticity, Markov chains
Metal alloys are uniquely strong, ductile and recyclable. Predicting how metal components fail remains a grand challenge, limiting performance, reducing lifetime and raising emissions. We use ideas from condensed matter physics, dislocation theory and machine learning to discover the atomic mechanisms of plasticity and diffusion. A central focus is quantfying uncertainty in data-driven models spanning many time/length scales.

Recent Preprints / Papers (‡ = sole/corres.)
Entropy of dislocation glide (Rodney Group / PAFI) ArXiv 2024
‡Implicit differentiation in MD (Ivan Maliyov) ArXiv 2024 code
‡Misspecification uncertainty for low-noise models ArXiv 2024
MACE foundation model (Csyani Group, Section A.13) ArXiv 2023
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‡Free energies from mean-field bonds (Raynol Dsouza) Phys. Rev. B 2024
‡Coarse-graining & forecasting MD with descriptors Phys. Rev. Lett. 2023
‡Embedded ab initio with QM/ML (Petr Grigorev) Acta Mat. 2023
A15 defects in FCC (Marinica Group / TAMMBER) Nat. Comm. 2023
‡Ill-conditioned Markov chains (Wales Group / PyGT) Proc. Roy. Soc. 2023

Recent & Upcoming Invited Conferences/Seminars
CoMPASs workshop, ICMS, Edinburgh, 11/25
Senior Scholar, IPAM, UCLA, 9/25-12/25
CECAM workshop, Lausanne, 03/25
AppliedML 2025, EPFL, 02/25

MRS Fall Meeting, Boston, 12/24
General Physics Seminar, École Polytechnique, 11/24
International Nuclear Engineering Consortium, MIT/Oxford, 11/24
Engineering Seminar, U Oxford, 10/24
Chemistry Seminar, U Cambridge, 10/24
UQ Meeting, Max Planck Magdeburg, 8/24
Hattrick-Simpers Group Seminar, U Toronto, 7/24
CIMTEC Conference, Tuscany, 6/24
Senior Scholar, IMSI, U Chicago, 3/24-6/24 (video)